1. Field of the Invention
The present invention relates to processing of data sets collected from satellites in Earth orbit. More particularly, the present invention relates to an apparatus and method allowing altimeter data from multiple satellites to be combined consistently.
2. Description of the Related Art
Observing the ocean circulation on length scales of 50 km requires the combination of data from several satellite altimeters. Recent altimeter missions such as the Navy Geosat-Exact Repeat Mission (Geosat-ERM), European Space Agency Earth Remote Sensor-1 (ERS-1), and NASA TOPEX/POSEIDON (T/P) satellites have flown during different time periods, and this is the main difficulty in using these data together.
The use of a single altimeter satellite in isolation has been solved. The distance from Earth""s reference ellipsoid to the surface of the ocean is defined as sea level (SL). The SL can be viewed as being composed of two parts: the geoid, which is constant in time, and deviations from the geoid due to oceanographic effects such as tides, currents, and thermal expansion. The ocean surface height above the geoid is the sea surface height (SSH). Over distances greater than a few kilometers, the marine geoid is capable of causing SL variations that are orders of magnitude greater than the contribution to SL caused by oceanographic effects (see FIG. 1).
For an altimeter in a repeat orbit, observing SL variations due to changes in the ocean about the mean state is straightforward. Since the geoid is constant in time, subtracting the mean over time at each point along the satellite""s ground track removes the geoid influence. This process (known as collinear analysis) also removes the mean SSH but retains oceanic SSH variations about the mean. Observing SSH variability through collinear analysis has been the primary mode of applying altimetry to oceanographic observation. The key component that is required for using a single altimeter satellite is the mean SSH along the satellite ground tracks. Developing the mean SSH for each altimeter satellite in isolation has been done.
The main problem restricting the use of data from several satellite altimeters simultaneously is that the mean SSH to which different altimeter satellites are referenced has been measured over different time periods. For example, the Geosat-ERM satellite measured the SSH from 1986-1989, while T/P measured the SSH from 1992 to 1998. There are significant SSH changes between these time periods. The Navy Geosat Follow-On (GFO) mission, launched in February 1998, overlies the same ground tracks as the prior Geosat-Exact Repeat Mission. However, the SSH deviation measured by the GFO satellite may not be used consistently with the data from the T/P satellite unless some common reference is established for comparison. The missing piece of information is the SSH difference between the mean SSH over each time period. The present invention constructs the SSH difference between satellite altimeter missions.
The difficulty in producing the SSH change from one satellite mission to another is the different marine geoid sampled along the ground tracks of the various altimeter missions. Recent altimeters have been in different repeat orbits, sampling different geographical points and hence different locations on the marine geoid. One method for combining multiple altimeter data sets is to use a geoid produced from independent data sources. Many geoids exist on the basis of gravimetric data. However, on the scales of the ground track spacing of different altimeter missions (up to 157 km for the T/P and the Geosat-ERM or the T/P and the ERS-1 ground tracks) (see FIG. 2), the errors in the gravimetric geoids can be larger than the signal due to SSH features of interest. The geoid errors are due to bathymetric features such as seamounts and trenches that have large spatial gradients and are poorly resolved by existing data sets.
To remove geoid errors to an acceptable level, the SSH must be smoothed to scales larger than 2000 km. While significant ocean circulation does exist on scales greater than 2000 km, information on the smaller scales would be lost. In particular, the most important portion of the ocean variability for real-time applications, the eddy mesoscale field, would be completely removed. The loss in small-scale features is undesirable and can be avoided through the use of the present invention. The geoid at points where ground tracks of different missions cross is the same for both missions. Thus, SSH changes at these points may be accurately observed.
The most serious error source contaminating the observed SSH change between separate altimeter missions is the Geographically Correlated Orbit Error (GCOE) of each satellite. This error is fixed for a given satellite. The GCOE at any point along an altimeter ground track is an integration of orbit solution errors (mainly due to gravity model errors) along the ground track up to that point. At a point where two ground tracks of a single satellite cross, the integral of errors along each track to the crossover point is different for the two tracks, and so the GCOE is different for the two tracks. Thus, crossover differences provide one method of measuring GCOE. However, certain spatial functions cannot be observed by crossover differences. For example, a constant bias error along all ground tracks produces no crossover differences.
The present invention removes the measurable portion of GCOE from satellite altimeter data, thereby allowing such data from independent satellite missions to be combined consistently. First, the GCOE structure for a reference data set is estimated through a crossover difference analysis. At the same time, the spatial GCOE structure that is not observable (and thus not removed) through the crossover analysis is determined. This improves upon prior approaches, which have not taken the unobservable GCOE into account.
The reference mean SL corrected for the observable GCOE is used as a reference surface for estimating the GCOE structure for altimeter data from an independent satellite mission (input data set). This is performed by examining the differences between the altimeter mean SL and the reference mean SL at the multimission crossover points. The resulting SSH differences at the multimission crossover points then leads to the SSH change between the reference data mission and the input data altimeter mission. This SSH change allows data from different input data altimeter missions to be used together.
In a first aspect, the present invention provides a system for generating reference sea level data. An apparatus embodying the system comprises a storage and a processor. The storage stores altimeter data for a plurality of intersecting ground tracks of an altimeter satellite. The processor computes the reference sea level data based on the altimeter data and a filtered normal mode decomposition generated by removing unmeasurable modes from a normal mode decomposition of estimated error for the altimeter data.
In a second aspect, the invention provides a system for observing changes in sea surface height over time. A method embodying the invention comprises removing error from altimeter data based on estimated error between the altimeter data and reference sea level data from which measurable error has been removed but one or more unmeasurable modes of error have not been removed. Differences are computed between the reference sea level data and the altimeter data after the removing of error from the reference sea level data.